A number of technological and economic pressures currently exist in the development of new types of electronics. Recent advancements in quantum computing, MEMS, nanotechnology, and molecular and memristive electronics offer new and exciting avenues for extending the limitations of conventional von Neumann digital computers. As device densities increase, the cost of R&D and manufacturing has skyrocketed due to the difficulty of precisely controlling fabrication at such a small scale. New computing architectures are needed to ease the economic pressures described by what has become known as Moore's second law: The capital costs of semiconductor fabrication increases exponentially over time. We expend enormous amounts of energy constructing the most sterile and controlled environments on earth to fabricate modern electronics. Life however is capable of assembling and repairing structures of far greater complexity than any modern chip, and it is capable of doing so while embedded in the real world, and not a clean room.
IBM's cat-scale cortical simulation of 1 billion neurons and 10 trillion synapses, for example, required 147,456 CPUs, 144 TB of memory, and ran at 1/83rd real time. At a power consumption of 20 W per CPU, this is 2.9 MW. If we presume perfect scaling, a real-time simulation would consume 83× more power or 244 MW. At roughly thirty times the size of a cat cortex, a human-scale cortical simulation would reach over 7 GW. The cortex represents a fraction of the total neurons in a brain, neurons represent a fraction of the total cells, and the IBM neuron model was extremely simplified. The number of adaptive variables under constant modification in the IBM simulation is orders of magnitude less than the biological counterpart and yet its power dissipation is orders of magnitude larger. The power discrepancy is so large it calls attention not just to a limit of our current technology, but also to a deficiency in how we think about computing.
Brains have evolved to move bodies through a complex and changing world. In other words, brains are both adaptive and mobile devices. If we wish to build practical artificial brains with power and space budgets approaching biology, we must merge memory and processing into a new type of physically adaptive hardware.